
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase    :   Dict						=			logging.get_logger(__name__)

lowercase    :   Any						=			{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'mobilenet_v2'







	def __init__(   self						:List[Any]	,  a						:Optional[int]=3	,  a						:Optional[Any]=2_2_4	,  a						:Optional[int]=1.0	,  a						:Any=8	,  a						:Any=8	,  a						:Any=6	,  a						:Any=3_2	,  a						:Optional[int]=True	,  a						:Optional[Any]=True	,  a						:List[Any]="relu6"	,  a						:str=True	,  a						:Tuple=0.8	,  a						:Tuple=0.02	,  a						:List[str]=0.001	,  a						:int=2_5_5	,  **a						:Any	,  )						->					str:
								super().__init__(**a		)

								if depth_multiplier <= 0:
															raise ValueError("depth_multiplier must be greater than zero."		)

								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  Union[str, Any]       =   image_size
								__UpperCamelCase      :  str       =   depth_multiplier
								__UpperCamelCase      :  int       =   depth_divisible_by
								__UpperCamelCase      :  str       =   min_depth
								__UpperCamelCase      :  Tuple       =   expand_ratio
								__UpperCamelCase      :  Dict       =   output_stride
								__UpperCamelCase      :  Tuple       =   first_layer_is_expansion
								__UpperCamelCase      :  Any       =   finegrained_output
								__UpperCamelCase      :  List[Any]       =   hidden_act
								__UpperCamelCase      :  Dict       =   tf_padding
								__UpperCamelCase      :  str       =   classifier_dropout_prob
								__UpperCamelCase      :  List[str]       =   initializer_range
								__UpperCamelCase      :  Optional[Any]       =   layer_norm_eps
								__UpperCamelCase      :  Tuple       =   semantic_loss_ignore_index




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	version.parse('1.11')







	@property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Mapping[str, Mapping[int, str]]:
								return OrderedDict([("pixel_values", {0: "batch"})]		)







	@property
	def   _lowerCamelCase							(   self						:int		)						->					Mapping[str, Mapping[int, str]]:
								if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]		)
								else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]		)







	@property
	def   _lowerCamelCase							(   self						:Tuple		)						->					float:
								return 1E-4

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase    :   List[str]						=			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			[
		    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GPTBigCodeForSequenceClassification',
		    'GPTBigCodeForTokenClassification',
		    'GPTBigCodeForCausalLM',
		    'GPTBigCodeModel',
		    'GPTBigCodePreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_bigcode import (
				    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTBigCodeForCausalLM,
				    GPTBigCodeForSequenceClassification,
				    GPTBigCodeForTokenClassification,
				    GPTBigCodeModel,
				    GPTBigCodePreTrainedModel,
				)


else:
		import sys

		lowercase    :   Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)